A Clustering Strategy for the Key Segmentation of Musical Audio


Key changes are common in Western classical music. The precise segmentation of a music piece at instances where key changes occur allows for further analysis, like self-similarity analysis, chord recognition, and several other techniques that mainly pertain to the characterization of music content. This article examines the automatic segmentation of audio data into parts composed in different keys, using clustering on chroma-related spaces. To this end, the k-means algorithm is used and a methodology is proposed so that useful information about key changes can be derived, regardless of the number of clusters or key changes. The proposed methodology is evaluated by experimenting on the segmentation of recordings of existing compositions from the Classic-Romantic repertoire. Additional analysis is performed using artificial data sets. Specifically, the construction of artificial pieces is proposed as a means to investigate the potential of the strategy under discussion in predefined key-change scenarios that encompass different musical characteristics. For the existing compositions, we compare the results of our proposed methodology with others from the music information retrieval literature. Finally, although the proposed methodology is only capable of locating key changes and not the key identities themselves, we discuss results regarding the labeling of a composition's key in the located segments.